Industrial controllers are special-purpose computers utilized for controlling industrial processes, manufacturing equipment, and other factory automation, such as data collection or networked systems. Logic processors, such as Programmable Logic Controllers (PLCs) or PC-based controllers, are at the core of the industrial control system. Industrial controllers are typically programmed by systems designers to operate manufacturing processes via user-designed logic programs or user programs. The user programs are stored in memory and can be executed by the industrial controller in a generally sequential manner although instruction jumping, looping and interrupt routines are also common. The user program is typically associated with a plurality of memory elements, registers, and/or variables that provide dynamics to controller operations and programs. Different types of industrial controllers are often distinguished by the number of inputs and outputs (I/O) the controllers are able to process, the amount of memory, the number and type of instructions supported by the controller's instruction palette, and speed of the controller's central processing unit (CPU).
Industrial controllers interact with field devices on the plant floor to control automated processes relating to such objectives as product manufacture, material handling, batch processing, supervisory control, and other such applications. Industrial controllers store and execute the user-defined control programs to effect decision-making in connection with the controlled process. Such programs can include, but are not limited to, ladder logic, sequential function charts, function block diagrams, structured text, or other such platforms
In a more macro sense relative to the controller, businesses have become progressively more complex in that higher order business systems or computers often need to exchange data with such industrial controllers. For instance, an industrial automation enterprise may include several plants in different locations. Driven by such considerations as increased efficiency, productivity improvement, and cost-reduction, manufacturers are becoming more interested in collecting, analyzing, and optimizing data and metrics from global manufacturing sites. For example, a food company may have several plants located across the globe for producing a certain brand of food. In the past, these factories were standalone and generally isolated from one another, rendering data collection and comparison of metrics between facilities difficult. In today's networked world, manufacturers are demanding real-time data from their factories to drive optimization and productivity.
Moreover, manufacturers are faced with a growing obligation to comply with regulatory record-keeping and reporting (e.g., the Food and Drug Administration's CFR Part 11 requirements, emissions reporting, quality reporting, etc.). Some industrial systems must also leverage historical data in order to perform process analytics, such as comparing current process states with prior states (e.g., batch comparison, process optimization etc.), analyzing production or machine metrics over time, creating totalizers (e.g., means, max, etc.), performing advanced analysis using historical data to optimize a current process in real time, or other such operations. To collect industrial data necessary for such analytics, some systems employ a PC-historian, which is an industrial computer configured to capture data from industrial controllers.
However, there are disadvantages to existing data collection and storage solutions. For example, conventional PC-historians are not tightly integrated with standard control systems, limiting overall data collection performance and abilities. PC-historians are generally applied on the back-end of system design, and are therefore loosely coupled or integrated within the framework of the control architecture. The relatively loose integration between historians and the control system can render configuration and deployment more complex and costly. This can also complicate the process of identifying which sets of data should or should not be captured.
There are also difficulties associated with mapping and integrating historians into a larger enterprise. For example, an enterprise may employ a common scheme that defines security for underlying control components of an industrial system. Since current historian systems are conventionally applied outside the control system framework, historian components may, at best, provide their own security implementation but may not be integrated in the enterprise's security framework with other similarly situated components, including enterprise control components at higher levels of the enterprise.
The above-described deficiencies of today's industrial control systems are merely intended to provide an overview of some of the problems of conventional systems, and are not intended to be exhaustive. Other problems with conventional systems and corresponding benefits of the various non-limiting embodiments described herein may become further apparent upon review of the following description.